Predicting life expectancy#

# TBA: header image

It is no secret that life expectancy has been increasing rapidly over the past couple of decades. A crucial indicator of a nation’s health and well-being can be traced back to its life expectancy statistic. This statistic is influenced by a multitude of factors, such as: economic stability, healthcare quality lifestyle, education, environmental conditions and many more. The question begs however, which of these factors contribute the most to a nation’s life expectancy? One might argue that only education plays a role, because all other factors are dependent on it. Another person might argue that not all of these factors are dependent on a nation’s level of education, thus its impact might not be as significant as one expects. This project aims to put these two perspectives to the test, by analyzing several key factors contributing to a nation’s life expectancy.

Several datasets about factors related to life expectancy are used in this project. Using sophisticated modeling techniques and visualization, relevant data of these factors are compared to eachother. The objective is to provide both perspectives with sufficient arguments to defend their statement. The insights provided in this project may help determine whether education is the only factor contributing to life expectancy.

It is important to note the pace at which life expectancy has skyrocketed over the past decades. The extraordinary rise is attributed to a wide range of advances in human development. At the start of the nineteenth century, no region had a life expectancy higher than 40 years. Nowadays, multiple countries are close to hitting 80 years, according to ourworldindata. This rapid increase in life expectancy can be visualized using a box plot.

%run life_expectancy_boxplot.ipynb
Year
1950    257
1987    257
2004    257
2003    257
2002    257
2001    257
2000    257
1999    257
1998    257
1997    257
1995    257
1994    257
1993    257
1992    257
1991    257
1990    257
1989    257
2005    257
2006    257
2007    257
2016    257
1951    257
2021    257
2020    257
2019    257
2018    257
2017    257
2015    257
2008    257
2014    257
2013    257
2012    257
2011    257
2010    257
2009    257
1988    257
1996    257
1986    257
1968    257
1966    257
1965    257
1964    257
1963    257
1962    257
1961    257
1960    257
1985    257
1958    257
1957    257
1956    257
1955    257
1954    257
1953    257
1952    257
1967    257
1959    257
1969    257
1970    257
1983    257
1982    257
1981    257
1980    257
1979    257
1978    257
1977    257
1976    257
1975    257
1974    257
1973    257
1972    257
1971    257
1984    257
1940     46
1930     45
1900     43
1920     36
1937     35
1947     33
1910     33
1935     32
1945     32
1949     32
1948     31
1931     30
1927     30
1941     30
1942     30
1946     30
1925     30
1921     29
1932     28
1926     28
1938     27
1943     27
1923     27
1922     26
1928     26
1933     26
1944     26
1939     25
1905     25
1934     25
1936     25
1913     25
1924     24
1929     24
1911     24
1915     22
1901     22
1908     21
1902     20
1895     20
1918     19
1885     19
1907     19
1909     18
1912     18
1897     18
1906     18
1896     18
1903     18
1917     17
1904     17
1875     17
1919     17
1890     17
1881     17
1891     17
1892     16
1880     16
1882     16
1899     16
1916     16
1914     16
1870     16
1886     15
1894     15
1887     15
1898     15
1893     15
1889     14
1888     14
1884     14
1883     14
1876     13
1879     13
1878     13
1877     13
1873     12
1850     12
1865     12
1871     11
1872     11
1874     11
1868     11
1855     11
1861     11
1869     10
1859     10
1851     10
1856     10
1857     10
1858     10
1860     10
1862     10
1863     10
1864     10
1866     10
1867     10
1854      9
1853      9
1852      9
1846      8
1845      8
1849      8
1847      8
1848      8
1844      7
1843      7
1842      7
1841      7
1770      6
1838      5
1835      4
1839      4
1840      4
1820      4
1823      3
1800      3
1818      3
1825      3
1775      3
1830      3
1837      3
1831      3
1833      3
1836      3
1828      3
1783      2
1788      2
1778      2
1808      2
1793      2
1798      2
1768      2
1763      2
1803      2
1758      2
1753      2
1834      2
1773      2
1832      2
1813      2
1765      2
1815      2
1829      2
1816      2
1795      2
1817      2
1819      2
1785      2
1821      2
1822      2
1805      2
1827      2
1824      2
1755      2
1826      2
1573      1
1603      1
1623      1
1618      1
1543      1
1613      1
1608      1
1548      1
1598      1
1568      1
1553      1
1558      1
1593      1
1628      1
1583      1
1578      1
1563      1
1588      1
1814      1
1633      1
1698      1
1743      1
1738      1
1733      1
1728      1
1723      1
1718      1
1713      1
1708      1
1703      1
1693      1
1638      1
1688      1
1683      1
1678      1
1673      1
1668      1
1663      1
1658      1
1653      1
1643      1
1648      1
1772      1
1812      1
1764      1
1779      1
1777      1
1776      1
1774      1
1771      1
1769      1
1767      1
1766      1
1762      1
1811      1
1761      1
1760      1
1759      1
1757      1
1756      1
1754      1
1752      1
1751      1
1780      1
1781      1
1782      1
1784      1
1810      1
1809      1
1807      1
1806      1
1804      1
1802      1
1801      1
1799      1
1797      1
1796      1
1794      1
1792      1
1791      1
1790      1
1789      1
1787      1
1786      1
1748      1
Name: count, dtype: int64

Only education and GDP have an impact on life expectancy#

As seen in the dataset, it’s very common for countries with a good education to also have a high life expectancy. To make it more clear, the data can be visualized in this Bivariate Choropleth:

In this image, the left side of the legend is the education level, and the right side is the life expectancy. As shown, almost all countries with good education quality also have a high life expectancy. The reasoning behind this might be that people with better education tend to choose for a healthier way of life. It can also be visualized in the following way. This plot shows the rate in which people finish primary and secondary school, compared to the life expectancy of said person. This graph makes clear that people with better education tend to have a higher life expectancy. A reason for this increase in life expectancy comes from the fact that people with a better education make better choices. (Raghupathi & Raghupathi, 2020b)

Education level#

Assuming education is the only factor that predicts life expectancy in a country, a closer assessment is needed to determine which sector should be invested in.

Second plot#

GDP Argument (Second argument)#

We can also argue that a society with a good education will produce an increasing GDP. Research at the university of Munich has shown that people with a better education are able to achieve jobs with more complex skill sets, resulting in a higher paying job. If people in a society are able to keep higher paying jobs, the GDP from the country of origin will increase. This in turn will influence the life expectancy of a country. Research originating from the University of Zagreb has shown that an increase in GDP of a country, also has a positive influence on the country’s life expectancy. This is confirmed when you convert the data into a Bivariate Chropleth or a scatter plot (with a regression). These charts show the GDP of a country and the country’s life expectancy. This means that the increase in education gives an increase in GDP which delivers an increase in life expectancy.

../_images/26a812fa259c814efdbec03faf24ef3c55d183a7c19e04ce190b17a72ea0d178.png

Life expectancy cannot be predicted by just education#

Even though A country investing in their education program results in an increase in life expectancy. There are more direct approaches to increasing a country’s life expectancy. One possible solution is investing in increasing the country’s vaccination rate. Diseases or viruses like Polio and Diphtheria can be fatal if not treated appropriately, in some cases (like for polio) there is no cure at all. Not treating these diseases results in a drastic decrease in life expectancy. So instead of investing in education to improve life expectancy, a country should invest in vaccines as this has a more direct effect. This can be seen in the plot where it shows an increase in vaccination rate for polio and Diphtheria corresponds with an increase in life expectancy. This is also found in the research by Jenifer Ehreth. Which concludes that improving the vaccination rate is a big factor in increasing a country’s life expectancy. https://www.sciencedirect.com/science/article/pii/S0264410X03003773

Unhealthy lifestyles#

The prevelance of unhealthy lifestyles in (developed) countries may also contribute to life expectancy.

Counter argument 2#

Another way to increase life expectancy is to invest in cleaner and safer drinking water. Unsafe drinking water is the cause of a lot of different diseases, all of which can cause a person to live a shorter life. It can be seen in the graph that an increase in the amount of people that drink from a safe water source correlates with an increase in life expectancy, this also supported by the following research paper, Angelakis et al. (2021b). This means that it should be useful for a country to invest in a clean water source before it starts to invest in different areas.

The impact of vaccination#

Another factor to consider is

Conclusion#

hier moet nog een conclusie komen

References#

  1. Raghupathi, V., & Raghupathi, W. (2020). The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives Of Public Health, 78(1). https://doi.org/10.1186/s13690-020-00402-5

  2. Ehreth, J. (2003). The value of vaccination: a global perspective. Vaccine, 21(27–30), 4105–4117. https://doi.org/10.1016/s0264-410x(03)00377-3

  3. Angelakis, A. N., Vuorinen, H. S., Nikolaidis, C., Juuti, P. S., Katko, T. S., Juuti, R. P., Zhang, J., & Samonis, G. (2021). Water Quality and Life Expectancy: Parallel Courses in Time. Water, 13(6), 752. https://doi.org/10.3390/w13060752